Non-Linear Self-Interference Cancellation via Tensor Completion

TL;DR

This paper introduces a tensor completion-based method called CSID for non-linear self-interference cancellation in full-duplex communications, demonstrating high effectiveness and potentially lower computational complexity compared to traditional approaches.

Contribution

It applies low-rank tensor completion to non-linear SI cancellation, offering a novel approach that improves modeling accuracy and computational efficiency.

Findings

CSID effectively models and cancels non-linear SI signals.

CSID can have lower computational complexity than existing methods.

Increased memory requirements are a trade-off for improved performance.

Abstract

Non-linear self-interference (SI) cancellation constitutes a fundamental problem in full-duplex communications, which is typically tackled using either polynomial models or neural networks. In this work, we explore the applicability of a recently proposed method based on low-rank tensor completion, called canonical system identification (CSID), to non-linear SI cancellation. Our results show that CSID is very effective in modeling and cancelling the non-linear SI signal and can have lower computational complexity than existing methods, albeit at the cost of increased memory requirements.